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LEARNING TEXT EDITING TASKS FROM EXAMPLES: A PROCEDURAL APPROACH

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Author
Mo, Dan H.
Witten, Ian H.
Accessioned
2008-02-27T22:28:57Z
Available
2008-02-27T22:28:57Z
Computerscience
1999-05-27
Issued
1990-06-01
Subject
Computer Science
Type
unknown
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Abstract
Reformatting blocks of semi-structured information is a common editing task that typically involves highly repetitive actions sequences, but ones where exceptional cases arise constantly and must be dealt with as they arise. This paper describes a procedural programming-by-example approach to repetitive text editing which allows users to construct programs within a standard editing interface and extend them incrementally. Following a brief practice period during which they settle on an editing strategy for the task at hand, users commence editing in the normal way. Once the first block of text has been edited, they inform the learning system which constructs a generalized procedure from the actions that have been recorded. The system then attempts to apply the procedure to the next block of text, by predicting editing actions and displaying them for confirmation. If the user accepts a prediction, the action is carried out (and the program may be generalized accordingly); otherwise the user is asked to intervene and supply additional information, in effect debugging the program on the fly. A pilot implementation is described that operates in a simple interactive point-and-click editor (MacIntosh MINIEDIT), along with its performance on three sample tasks. In one case the procedure was learned correctly from the actions on the first text block, while in the others minor debugging was needed on subsequent text blocks. In each case a much smaller number of both keystrokes and mouse-clicks was required compared with normal editing, without the system making any prior assumptions about the structure of the text except for some general knowledge about lexical patterns. Although a smooth interactive interface has not yet been constructed, the results obtained serve to indicate the potential of this approach for semi-structured editing tasks.
Notes
We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at digitize@ucalgary.ca
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University of Calgary
Faculty
Science
Doi
http://dx.doi.org/10.11575/PRISM/31165
Uri
http://hdl.handle.net/1880/46180
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